{"id":38933,"date":"2026-05-10T14:00:00","date_gmt":"2026-05-10T14:00:00","guid":{"rendered":"https:\/\/www.aurorainbox.com\/?p=38933"},"modified":"2026-05-10T14:00:00","modified_gmt":"2026-05-10T14:00:00","slug":"que-es-rag-chatbot-whatsapp","status":"publish","type":"post","link":"https:\/\/www.aurorainbox.com\/en\/2026\/05\/10\/what-is-a-rag-chatbot-on-whatsapp\/","title":{"rendered":"What is RAG and how does it train WhatsApp chatbots with your knowledge base (2026)"},"content":{"rendered":"

Retrieval-augmented generation (RAG) is the technique that allows an AI agent to base its responses on your specific knowledge base\u2014PDFs, website, catalogs\u2014instead of responding with its general, trained knowledge. For a WhatsApp chatbot in 2026, RAG is the difference between an agent that recommends your actual products and policies versus one that conjures up competitor names and made-up prices.<\/p>\n

What is RAG in simple terms?<\/h2>\n

When a customer writes to your chatbot "What is the return policy?", there are two ways to respond:<\/p>\n

    \n
  1. Without RAG:<\/strong> The LLM responds based on their trained general knowledge \u2014 something generic like "typically return policies are 30 days."<\/li>\n
  2. With RAG:<\/strong> The system first searches in YOUR return policy document, retrieves the exact ticket and responds "According to our policy, you have 14 calendar days from delivery to return the product unused, with tags and original packaging."<\/li>\n<\/ol>\n

    RAG brings the precise and specific AI agent to your business.<\/p>\n

    How RAG works step by step<\/h2>\n
      \n
    1. Intake.<\/strong> You upload your documents (PDF, DOCX, website). The system breaks them down into passages of 200-500 words.<\/li>\n
    2. Embedding.<\/strong> Each passage becomes a numerical vector (an "embedding") that captures the semantic meaning.<\/li>\n
    3. Storage.<\/strong> The vectors are stored in a vector database (MongoDB Atlas, Pinecone, Azure AI Search).<\/li>\n
    4. Consultation.<\/strong> When a question arrives, it also becomes a vector and the closest semantically relevant passage is searched for.<\/li>\n
    5. Generation.<\/strong> The relevant passages are sent to the LLM along with the question. The LLM responds based on those passages.<\/li>\n<\/ol>\n

      All of this happens in less than 1 second per response.<\/p>\n

      Types of searches in RAG<\/h2>\n\n\n\n\n\n\n\n
      Guy<\/th>\nHow it works<\/th>\nWhen to use<\/th>\n<\/tr>\n<\/thead>\n
      Vectorial (semantics)<\/td>\nSearch by meaning<\/td>\nVague or rephrased questions<\/td>\n<\/tr>\n
      BM25 (keywords)<\/td>\nSearch by exact terms<\/td>\nQuestions with specific terms<\/td>\n<\/tr>\n
      Hybrid (vectorial + BM25)<\/td>\nCombine both with reciprocal rank fusion<\/td>\nThe standard in 2026<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      Aurora Inbox uses hybrid vector search + BM25 for maximum accuracy.<\/p>\n

      What documents to upload to RAG<\/h2>\n

      Minimum recommended for a WhatsApp chatbot:<\/p>\n